Development of a chub mackerel with less-aggressive fry stage by genome editing of arginine vasotocin receptor V1a2

Genome editing is a technology that can remarkably accelerate crop and animal breeding via artificial induction of desired traits with high accuracy. This study aimed to develop a chub mackerel variety with reduced aggression using an experimental system that enables efficient egg collection and genome editing. Sexual maturation and control of spawning season and time were technologically facilitated by controlling the photoperiod and water temperature of the rearing tank. In addition, appropriate low-temperature treatment conditions for delaying cleavage, shape of the glass capillary, and injection site were examined in detail in order to develop an efficient and robust microinjection system for the study. An arginine vasotocin receptor V1a2 (V1a2) knockout (KO) strain of chub mackerel was developed in order to reduce the frequency of cannibalistic behavior at the fry stage. Video data analysis using bioimage informatics quantified the frequency of aggressive behavior, indicating a significant 46% reduction (P = 0.0229) in the frequency of cannibalistic behavior than in wild type. Furthermore, in the V1a2 KO strain, the frequency of collisions with the wall and oxygen consumption also decreased. Overall, the manageable and calm phenotype reported here can potentially contribute to the development of a stable and sustainable marine product.


Computer software for video data analysis
The supplementary information describes the principle underlying the detection of abnormal behavior by image analysis software in detail.

Overview
The image analysis software is based on bioimage informatics technology, and automatically detects the data part where abnormal behavior was seen in the video. It took advantage of the habit of chub mackerel fry, which normally swim in groups. The image analysis software was programmed using the C ++ programming language.
The analysis software comprised several steps, as follows. First, it decomposed the video of 30 frames/s into a sequence of video frames. Second, it detected individual fry in the video frame. Third, it tracked the group of fry across frames to obtain the motion of each fry. Fourth, it represented the motion of the fry group in each video frame as a single feature vector. Till this step, we could have feature vectors for video frames.
An hour (3,600 seconds)-long video generated 108,000 frames, and consequently, we had = 108,000 vectors. Finally, using an anomaly detection method, called k-nearest neighbor (kNN), feature vectors showing some anomaly were detected. The number of the detected feature vectors indicates the number of anomalous behaviors, i.e., cannibalistic behaviors, in the video (Fig. 5b). Furthermore, in order to classify the detected abnormal behaviors as true cannibalistic behavior, collision with the wall, or others (such as panic state due to surprise), all detected frames were double-checked through visual inspection by human experts.

Fry detection in each frame
The location of individual fry was detected by combining two simple imageprocessing techniques, namely background subtraction and binarization, as shown in Supplementary Fig. S1a. The former considered the difference between a target video frame and a background image captured without the fry. Since the pool environment was static, background subtraction could enhance the region of individual fry. By taking the absolute value of difference for each pixel, a grayscale difference image was obtained. The latter was then applied to the difference image to determine the region of each fry. Finally, the location of the fry could be identified by the center of gravity of each fry region.

Tracking fry across frames
Fry were tracked in two consecutive frames. Among the various matching algorithms, we used the stable marriage algorithm, which is a ranking-based matching algorithm between two sets ( Supplementary Fig. S1b). While tracking 15 fry, for example, an individual (fry 1-15) detected in frame t would have a matching preference ranking based on its distance from an individual (fry # a-o) detected in the next frame t+1. The stable marriage algorithm searches for the most stable match, which would be the least inconvenient for this ranking. Tracking trajectories of the whole fry group were obtained by repeating this matching process for all consecutive frames ( Supplementary Fig. S1c).

Representing the motions of fry as a feature vector
Based on the tracking results of each fry using the stable marriage algorithm, one feature vector representing the tracking results of all individuals was calculated in each frame. For this feature vector representation, the direction in which each fry swam (that is, the direction of movement) and the relevant speed (i.e., the distance traveled within the frame) were first quantized into 12 levels and four levels, respectively. The result was plotted in 48 bins of the 2D polar histogram, as shown in Supplementary Fig. S1d.
The 2D polar histogram was then modified to be "rotation-invariant" so that the motions shown in Supplementary Fig. S1d (a) and (b) have to be treated as similar. For example, in (a) and (b), the fry swam in a group in a certain direction, but the directions were opposite; therefore, the distance between the two frames increased. The pool was circular, and the absolute direction of motion in the pool was not important for detecting anomalous motions. Therefore, we made this histogram rotation-invariant by rotating the whole histogram so that the quantized direction with the most moves reached the top, as shown in Supplementary Fig. S1e. Using this operation, we could expect the motions shown in Supplementary Fig. S1d (a) and (b) to have very similar histograms. Next, we re-quantized the angle into four levels and obtained a 2D polar histogram with 16 bins (Supplementary Fig. S1e). Finally, the 16 elements of this histogram form a 16dimensional vector at each frame.

Anomaly detection
By repeating the above process for all frames, we generated 16-dimensional vectors in each frame. Our goal was to detect the vectors showing anomalous motions.
The key idea of anomaly detection was that an anomalous motion becomes an "outlier," which is a vector very different from other vectors, as shown in Supplementary Fig. S1f. Vectors during cannibalistic behaviors are different from those of normal behaviors because cannibalistic behaviors often show very large and scattered motions of fry.
We employed kNN as an anomaly detection method. kNN evaluates the difference